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  • (-) WSL Research Units = Mountain Hydrology and Mass Movements
  • (-) Publication Year = 2019
  • (-) Keywords ≠ approval
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  • (-) WSL Authors = Bogner, Konrad
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The value of subseasonal hydrometeorological forecasts to hydropower operations: how much does preprocessing matter?
Anghileri, D., Monhart, S., Zhou, C., Bogner, K., Castelletti, A., Burlando, P., & Zappa, M. (2019). The value of subseasonal hydrometeorological forecasts to hydropower operations: how much does preprocessing matter? Water Resources Research, 55(12), 10159-10178. https://doi.org/10.1029/2019WR025280
Machine learning techniques for predicting the energy consumption/production and its uncertainties driven by meteorological observations and forecasts
Bogner, K., Pappenberger, F., & Zappa, M. (2019). Machine learning techniques for predicting the energy consumption/production and its uncertainties driven by meteorological observations and forecasts. Sustainability, 11(12), 3328 (22 pp.). https://doi.org/10.3390/su11123328
Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach
Monhart, S., Zappa, M., Spirig, C., Schär, C., & Bogner, K. (2019). Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach. Hydrology and Earth System Sciences, 23(1), 493-513. https://doi.org/10.5194/hess-23-493-2019